Visualization Tool for qPCR Genotyping Data

ABSTRACT

Systems and methods are used to display data obtained from a qPCR instrument. Each of two or more samples is probed with a first labeling probe and a second labeling probe. A first data set is received from a qPCR instrument at a first cycle number that includes for each sample a first labeling probe intensity, and a second labeling probe intensity. A second data set is received at a second cycle number that includes for each sample a first labeling probe intensity and a second labeling probe intensity. A first plot of first labeling probe intensity as a function of second labeling probe intensity is created using the first data set. A second plot of first labeling probe intensity as a function of second labeling probe intensity is created using the second data set. The first plot and the second plot are displayed in response to user defined input to provide dynamic and real-time analysis of genotyping data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional applicationSer. No. 13/083,476 filed Apr. 8, 2011, which claims priority to U.S.Provisional Application No. 61/322,749 filed Apr. 9, 2010, both of whichare incorporated herein by reference.

INTRODUCTION

Quantitative polymerase chain reaction (qPCR) instruments or cyclersallow data to be collected during each cycle. PCR data is typicallycollected at each cycle using an optical system within the qPCRinstrument that can detect electromagnetic radiation emitted by one ormore labeling probes attached to each nucleic acid sample analyzed bythe qPCR instrument. The PCR data, therefore, includes one or morelabeling probe intensity values for each sample at each cycle or at eachtime associated with a cycle.

Various embodiments of systems according to the present teachingsinclude a qPCR instrument, as well as a processor or computer forcontrolling and/or monitoring the qPCR instrument. The processor is usedto create and modify the experiment parameters sent to the qPCRinstrument and/or to monitor the qPCR instrument and analyze the PCRdata received from the qPCR instrument after the experiment. AlthoughqPCR systems receive and analyze the qPCR data, they generally onlydisplay useful or discriminatory information at the end-point or aftercompletion of the PCR experiment. Also, although the qPCR instrument andthe processor of a qPCR system can communicate across a network, eachqPCR system may include one qPCR instrument and one processor. Finally,although high throughput experimental workflows may require that thesame PCR experiment be run on batches of samples, qPCR systems may oftenrequire entry of experimental parameters for each batch placed in a qPCRinstrument.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings, described below,are for illustration purposes only. The drawings are not intended tolimit the scope of the present teachings in any way.

FIG. 1 is an exemplary flowchart showing a method for displaying dataobtained from a qPCR instrument, upon which embodiments of the presentteachings may be implemented.

FIG. 2 is a block diagram that illustrates a polymerase chain reaction(PCR) instrument, upon which embodiments of the present teachings may beimplemented.

FIG. 3 is a block diagram that illustrates a computer system, upon whichembodiments of the present teachings may be implemented.

FIG. 4 is a diagram of a system for networking thermal cyclinginstruments, upon which embodiments of the present teachings may beimplemented.

FIG. 5 is an exemplary amplification plot, upon which embodiments of thepresent teachings may be implemented.

FIG. 6 is an exemplary allelic discrimination plot of end-point datawhere allelic discrimination is apparent, upon which embodiments of thepresent teachings may be implemented.

FIG. 7 is an exemplary allelic discrimination plot of end-point datawhere allelic discrimination is not apparent, upon which embodiments ofthe present teachings may be implemented.

FIG. 8 is an exemplary allelic discrimination plot at cycle number 30from the same experiment that produced the data of FIG. 6, upon whichembodiments of the present teachings may be implemented.

FIG. 9 is an exemplary allelic discrimination plot at cycle number 35from the same experiment that produced the data of FIG. 6, upon whichembodiments of the present teachings may be implemented.

FIG. 10 is an exemplary allelic discrimination plot at cycle number 40from the same experiment that produced the data of FIG. 6, upon whichembodiments of the present teachings may be implemented.

FIG. 11 is an exemplary allelic discrimination plot at cycle number 40from the same experiment that produced the data of FIG. 6 showingtrajectory lines representing data from previous cycle numbers, uponwhich embodiments of the present teachings may be implemented.

FIG. 12 is an exemplary allelic discrimination plot at cycle number 40from the same experiment that produced the data of FIG. 7 showingtrajectory lines representing data from previous cycle numbers, uponwhich embodiments of the present teachings may be implemented.

FIGS. 13A-13C display the interactive nature of an exemplary userinterface, according to various embodiments of the present teachings.

FIGS. 14A-14C display the interactive nature of an exemplary userinterface, according to various embodiments of the present teachings.

DESCRIPTION OF VARIOUS EMBODIMENTS

Various embodiments of systems and methods of a visualization tool forqPCR genotyping data may provide an end user a dynamic display ofgenotyping data in response to end user input. The interactive nature ofvarious embodiments of systems and methods of the present teachingsallows an end user command of a significant amount of informationdisplayed as a time-based plot of the data, which may be accessed in astep-wise fashion from user input, or may be accessed as a video displayfrom user input. In various embodiments of systems and methods of avisualization tool for qPCR genotyping data, a sample table may beassociated with the sample plot, which sample table may have a widerange of information associated with each sample represented in thetable. In various embodiments of systems and methods of the presentteachings, an end user may select a sample or samples represented on thesample table in any fashion desired; each sample having a wealth ofsample information associated with it, and the selected sample orsamples may be displayed on the plot of genotyping data. Accordingly,through such a visualization tool, an end user may dynamicallyunderstand the impact of a variety of experimental conditions on theoutcome of a genotyping experiment. Such analysis may provide an enduser with the ability to, for example, but not limited by, troubleshootambiguous end-point data, make manual calls, enhance genotypeassignment, optimize assay and analysis conditions.

FIG. 1 is a flow diagram depicting various embodiments of systems andmethods of the present teachings for displaying and analyzing data fromgenotyping samples. As previously discussed, embodiments of systems onwhich embodiments of various methods may be implemented include qPCRinstrument and processor, which may be in communication. Thiscommunication can include the exchange of data or control information,for example.

As one of ordinary skill in the art is apprised, a PCR analysis isperformed on a thermal cycling instrument, which has various protocolsfor cycling though a plurality of thermal cycles in order to amplify agene target. In various embodiments of the present teachings, the numberof cycles performed for the amplification may be between about 20-40cycles. For various embodiments of the present teachings, the number ofcycles performed for the amplification may be greater than 40 cycles.For amplification of a gene target a thermal cycling instrument mayperform a first thermal cycle of a PCR experiment in a certain cycletime that may be associated with a first thermal cycle number.

In various embodiments of a genotyping analysis, two or more DNA samplesare probed with a first probe and a second probe. As indicated in step110 of FIG. 1, a processor may receive from a qPCR instrument based onany of a variety of protocols for data collection, a first data set at afirst time that includes for each of the two or more DNA samples a firstprobe intensity and a second probe intensity at the first time.According to various embodiments of the present teachings, and asindicated in step 120 of FIG. 1, a processor may receive from a qPCRinstrument based on any of a variety of protocols for data collection, asecond data set at a second time that includes for each of the two ormore DNA samples a first probe intensity and a second probe intensity atthe second time.

According to various embodiments of the present teachings, a userinterface may present to an end user a visualization tool for theanalysis of the data sets received a first time and a second time. Aspreviously mentioned, a plurality of samples may be processed forgenotyping analysis in a batch, yielding data-intense data sets. Variousembodiments of a systems and methods according to the present teachingsprovide for embodiments of a visualization tool that may assist an enduser in the evaluation and analysis of such data-intense data sets. Asindicated in step 140 of FIG. 1, for various embodiments of systems andmethods according to the present teachings, in response to input from anend user, a processor may generate a first plot of first probe intensityversus a second probe intensity using the first data set. Further, asindicated by step 145 of FIG. 1, a processor may generate a second plotof first probe intensity as a function of second probe intensity usingthe second data set in response to input from an end user. As indicatedin steps 150 and 155 of FIG. 1, according to various embodiments ofsystems and methods of the present teachings, a processor may displaythe first plot and the second plot in response to input from an enduser. In various embodiments, the input may be an interactive processwith a user interface to display the data in a step-wise fashion. Insuch embodiments, an end user may select any data set in any order fordisplay. For various embodiments, input from an end user can include,for example, clicking on or using any icon in a graphical userinterface, including but not limited to, a slider, scroll bar, knob,text box, and representation of a sample table. For various embodiments,the input may be a user selection to display the data as a video.

In various embodiments of systems and methods as indicated by FIG. 1, aprocessor may receive data during the run time of a PCR experiment. Forexample, a processor may receive the first data set from a qPCRinstrument after the collection of the first data set and beforecollection of the second data set. Further, this protocol may beextended throughout the run time, so that, for example, a processor mayreceive the second data set from a qPCR instrument after the collectionof the second data set and before collection of a subsequent data set.

According to various embodiments of systems and methods of the presentteachings, a processor may receive the first data set and the seconddata set from a qPCR instrument after thermal cycling has completed. Forexample, a processor may receive the first data set and the second dataset after it has been stored on a computer-readable medium.

According to various embodiments of systems and methods of the presentteachings, as indicated in FIG. 1, in response to user input, avisualization tool may assist an end user in the displaying of variousaspects of genotyping data sets, thereby facilitating in the analysis ofgenotyping data. In various embodiments, a processor may display a plotshowing trajectory lines between the second data set and the first dataset. In various embodiments, a processor may display on the first plotquality values for the first data set and displays on the second plotquality values for the second data set. According to variousembodiments, a user interface provides an interaction between selectionsmade on a sample table and dynamically displayed on a plot of genotypingdata. In various embodiments, selections made by an end user from a userinterface of a visualization tool may, for example, but not limited by,provide dynamic analysis for enabling an end user to, for example, butnot limited by, troubleshoot ambiguous end-point data, make manualcalls, use trajectory lines to assist in visualizing clusters to enhancegenotype assignment, optimize assay conditions (i.e. labeling probe,assay buffer, etc.) and optimize analysis conditions.

Various embodiments of methods and systems according to the presentteachings may utilize data sets that may be represented, for example,but not limited by, according to the graph depicted in FIG. 6. Such arepresentation may arise from analyses utilizing two dyes havingemissions at different wavelengths, which dyes can be associated witheach of a labeling probe directed at one of two alleles for a genomiclocus in a biological sample. In such duplex reactions, a discrete setof signals for each of three possible genotypes is produced. In aCartesian coordinate system of signal 2 versus signal 1, as shown inFIG. 6, each data point shown on such a graphic representation may havecoordinates in one of three discrete sets of signals given, for examplein reference to FIG. 6, as (signal 2, signal 2), for which a cluster ofdata points 610 is displayed, (signal 2, signal 1) for which a clusterof data points 630 is displayed, and (signal 1, signal 1). for which acluster of data points 620 is displayed. Accordingly, for each datapoint, a discrete set of signals for a plurality of samples may bestored as data points in a data set. Such data sets may be stored in avariety of computer readable media, and analyzed either dynamicallyduring analysis or post analysis, as will be discussed in more detailsubsequently.

One such type of assay used to demonstrate the features of embodimentsof methods and systems for the visualization of genotyping data canutilize TaqMan® reagents, and may use, for example, but not limited by,FAM and VIC dye labels, as will be discussed subsequently. However, oneof ordinary skill in the art will recognize that a variety of assaysincluding labeling probe reagents may be utilized to produce data thatmay be analyzed according to various embodiments of methods and systemsof the present teachings.

The term “labeling probe” generally, according to various embodiments,refers to a molecule used in an amplification reaction, typically forquantitative or qPCR analysis, as well as end-point analysis. Suchlabeling probes may be used to monitor the amplification of the targetpolynucleotide. In some embodiments, oligonucleotide labeling probespresent in an amplification reaction are suitable for monitoring theamount of amplicon(s) produced as a function of time. Sucholigonucleotide labeling probes include, but are not limited to, the5′-exonuclease assay TaqMan® labeling probes described herein (see alsoU.S. Pat. No. 5,538,848), various stem-loop molecular beacons (see e.g.,U.S. Pat. Nos. 6,103,476 and 5,925,517 and Tyagi and Kramer, 1996,Nature Biotechnology 14:303-308), stemless or linear beacons (see, e.g.,WO 99/21881), PNA Molecular Beacons™ (see, e.g., U.S. Pat. Nos.6,355,421 and 6,593,091), linear PNA beacons (see, e.g., Kubista et al.,2001, SPIE 4264:53-58), non-FRET labeling probes (see, e.g., U.S. Pat.No. 6,150,097), Sunrise®/Amplifluor® labeling probes (U.S. Pat. No.6,548,250), stem-loop and duplex Scorpion™ labeling probes (Solinas etal., 2001, Nucleic Acids Research 29:E96 and U.S. Pat. No. 6,589,743),bulge loop labeling probes (U.S. Pat. No. 6,590,091), pseudo knotlabeling probes (U.S. Pat. No. 6,589,250), cyclicons (U.S. Pat. No.6,383,752), MGB Eclipse™ probe (Epoch Biosciences), hairpin labelingprobes (U.S. Pat. No. 6,596,490), peptide nucleic acid (PNA) light-uplabeling probes, self-assembled nanoparticle labeling probes, andferrocene-modified labeling probes described, for example, in U.S. Pat.No. 6,485,901; Mhlanga et al., 2001, Methods 25:463-471; Whitcombe etal., 1999, Nature Biotechnology. 17:804-807; Isacsson et al., 2000,Molecular Cell Labeling probes. 14:321-328; Svanvik et al., 2000, AnalBiochem. 281:26-35; Wolffs et al., 2001, Biotechniques 766:769-771;Tsourkas et al., 2002, Nucleic Acids Research. 30:4208-4215; Riccelli etal., 2002, Nucleic Acids Research 30:4088-4093; Zhang et al., 2002Shanghai. 34:329-332; Maxwell et al., 2002, J. Am. Chem. Soc.124:9606-9612; Broude et al., 2002, Trends Biotechnol. 20:249-56; Huanget al., 2002, Chem Res. Toxicol. 15:118-126; and Yu et al., 2001, J. Am.Chem. Soc 14:11155-11161. Labeling probes can also comprise black holequenchers (Biosearch), Iowa Black (IDT), QSY quencher (MolecularLabeling probes), and Dabsyl and Dabcel sulfonate/carboxylate Quenchers(Epoch). Labeling probes can also comprise two labeling probes, whereinfor example a fluorophore is on one probe, and a quencher on the other,wherein hybridization of the two labeling probes together on a targetquenches the signal, or wherein hybridization on target alters thesignal signature via a change in fluorescence. Labeling probes can alsocomprise sulfonate derivatives of fluorescenin dyes with a sulfonic acidgroup instead of the carboxylate group, phosphoramidite forms offluorescein, phosphoramidite forms of CY 5 (available for example fromAmersham).

As used herein, the term “nucleic acid sample” refers to nucleic acidfound in biological samples according to the present teachings. It iscontemplated that samples may be collected invasively or noninvasively.The sample can be on, in, within, from or found in conjunction with afiber, fabric, cigarette, chewing gum, adhesive material, soil orinanimate objects. “Sample” as used herein, is used in its broadestsense and refers to a sample containing a nucleic acid from which a genetarget or target polynucleotide may be derived. A sample can comprise acell, chromosomes isolated from a cell (e.g., a spread of metaphasechromosomes), genomic DNA, RNA, cDNA and the like. Samples can be ofanimal or vegetable origins encompassing any organism containing nucleicacid, including, but not limited to, plants, livestock, household pets,and human samples, and can be derived from a plurality of sources. Thesesources may include, but are not limited to, whole blood, hair, blood,urine, tissue biopsy, lymph, bone, bone marrow, tooth, amniotic fluid,hair, skin, semen, anal secretions, vaginal secretions, perspiration,saliva, buccal swabs, various environmental samples (for example,agricultural, water, and soil), research samples, purified samples, andlysed cells. It will be appreciated that nucleic acid samples containingtarget polynucleotide sequences can be isolated from samples using anyof a variety of sample preparation procedures known in the art, forexample, including the use of such procedures as mechanical force,sonication, restriction endonuclease cleavage, or any method known inthe art.

The terms “target polynucleotide,” “gene target” and the like as usedherein are used interchangeably herein and refer to a particular nucleicacid sequence of interest. The “target” can be a polynucleotide sequencethat is sought to be amplified and can exist in the presence of othernucleic acid molecules or within a larger nucleic acid molecule. Thetarget polynucleotide can be obtained from any source, and can compriseany number of different compositional components. For example, thetarget can be nucleic acid (e.g. DNA or RNA). The target can bemethylated, non-methylated, or both. Further, it will be appreciatedthat “target” used in the context of a particular nucleic acid sequenceof interest additionally refers to surrogates thereof, for exampleamplification products, and native sequences. In some embodiments, aparticular nucleic acid sequence of interest is a short DNA moleculederived from a degraded source, such as can be found in, for example,but not limited to, forensics samples. A particular nucleic acidsequence of interest of the present teachings can be derived from any ofa number of organisms and sources, as recited above.

As used herein, “DNA” refers to deoxyribonucleic acid in its variousforms as understood in the art, such as genomic DNA, cDNA, isolatednucleic acid molecules, vector DNA, and chromosomal DNA. “Nucleic acid”refers to DNA or RNA in any form. Examples of isolated nucleic acidmolecules include, but are not limited to, recombinant DNA moleculescontained in a vector, recombinant DNA molecules maintained in aheterologous host cell, partially or substantially purified nucleic acidmolecules, and synthetic DNA molecules. Typically, an “isolated” nucleicacid is free of sequences which naturally flank the nucleic acid (i.e.,sequences located at the 5′ and 3′ ends of the nucleic acid) in thegenomic DNA of the organism from which the nucleic acid is derived.Moreover, an “isolated” nucleic acid molecule, such as a cDNA molecule,is generally substantially free of other cellular material or culturemedium when produced by recombinant techniques, or free of chemicalprecursors or other chemicals when chemically synthesized.

Computer and Instrument Systems

Various embodiments of systems and methods for the analysis ofgenotyping data according to the present teachings may utilize variousembodiments of a computer system depicted in the block diagrams shown inFIG. 2.

FIG. 2 is a block diagram that illustrates a computer system 200 thatmay be employed to carry out processing functionality, according tovarious embodiments, upon which embodiments of the present teachings maybe implemented. Computing system 200 can include one or more processors,such as a processor 204. Processor 204 can be implemented using ageneral or special purpose processing engine such as, for example, amicroprocessor, controller or other control logic. In this example,processor 204 is connected to a bus 202 or other communication medium.

Further, it should be appreciated that a computing system 200 of FIG. 2may be embodied in any of a number of forms, such as a rack-mountedcomputer, mainframe, supercomputer, server, client, a desktop computer,a laptop computer, a tablet computer, hand-held computing device (e.g.,PDA, cell phone, smart phone, palmtop, etc.), cluster grid, netbook,embedded systems, or any other type of special or general purposecomputing device as may be desirable or appropriate for a givenapplication or environment. Additionally, a computing system 200 caninclude a conventional network system including a client/serverenvironment and one or more database servers, or integration withLIS/LIMS infrastructure. A number of conventional network systems,including a local area network (LAN) or a wide area network (WAN), andincluding wireless and/or wired components, are known in the art.Additionally, client/server environments, database servers, and networksare well documented in the art.

Computing system 200 may include bus 202 or other communicationmechanism for communicating information, and processor 204 coupled withbus 202 for processing information.

Computing system 200 also includes a memory 206, which can be a randomaccess memory (RAM) or other dynamic memory, coupled to bus 202 forstoring instructions to be executed by processor 204. Memory 206 alsomay be used for storing temporary variables or other intermediateinformation during execution of instructions to be executed by processor204. Computing system 200 further includes a read only memory (ROM) 208or other static storage device coupled to bus 202 for storing staticinformation and instructions for processor 204.

Computing system 200 may also include a storage device 210, such as amagnetic disk, optical disk, or solid state drive (SSD) are provided andcoupled to bus 202 for storing information and instructions. Storagedevice 210 may include a media drive and a removable storage interface.A media drive may include a drive or other mechanism to support fixed orremovable storage media, such as a hard disk drive, a floppy disk drive,a magnetic tape drive, an optical disk drive, a CD or DVD drive (R orRW), flash drive, or other removable or fixed media drive. As theseexamples illustrate, the storage media may include a computer-readablestorage medium having stored therein particular computer software,instructions, and/or data.

In alternative embodiments, storage device 210 may include other similarinstrumentalities for allowing computer programs or other instructionsor data to be loaded into computing system 200. Such instrumentalitiesmay include, for example, a removable storage unit and an interface,such as a program cartridge and cartridge interface, a removable memory(for example, a flash memory or other removable memory module) andmemory slot, and other removable storage units and interfaces that allowsoftware and data to be transferred from the storage device 210 tocomputing system 200.

Computing system 200 can also include a communications interface 218.Communications interface 218 can be used to allow software and data tobe transferred between computing system 200 and external devices.Examples of communications interface 218 can include a modem, a networkinterface (such as an Ethernet or other NIC card), a communications port(such as for example, a USB port, a RS-232C serial port), a PCMCIA slotand card, Bluetooth, and the like. Software and data transferred viacommunications interface 218 are in the form of signals which can beelectronic, electromagnetic, optical or other signals capable of beingreceived by communications interface 218. These signals may betransmitted and received by communications interface 218 via a channelsuch as a wireless medium, wire or cable, fiber optics, or othercommunications medium. Some examples of a channel include a phone line,a cellular phone link, an RF link, a network interface, a local or widearea network, and other communications channels.

Computing system 200 may be in communication through communicationsinterface 218 to a display 212, such as a cathode ray tube (CRT), liquidcrystal display (LCD), and light-emitting diode (LED) display fordisplaying information to a computer user. In various embodiments,computing system 200, may be couple to a display through a bus. An inputdevice 214, including alphanumeric and other keys, is coupled to bus 202for communicating information and command selections to processor 204,for example. An input device may also be a display, such as an LCDdisplay, configured with touch screen input capabilities. Another typeof user input device is cursor control 216, such as a mouse, a trackballor cursor direction keys for communicating direction information andcommand selections to processor 204 and for controlling cursor movementon display 212. This input device typically has two degrees of freedomin two axes, a first axis (e.g., x) and a second axis (e.g., y), thatallows the device to specify positions in a plane. A computing system200 provides data processing and provides a level of confidence for suchdata. Consistent with certain implementations of embodiments of thepresent teachings, data processing and confidence values are provided bycomputing system 200 in response to processor 204 executing one or moresequences of one or more instructions contained in memory 206. Suchinstructions may be read into memory 206 from another computer-readablemedium, such as storage device 210. Execution of the sequences ofinstructions contained in memory 206 causes processor 204 to perform theprocess states described herein. Alternatively hard-wired circuitry maybe used in place of or in combination with software instructions toimplement embodiments of the present teachings. Thus implementations ofembodiments of the present teachings are not limited to any specificcombination of hardware circuitry and software.

The term “computer-readable medium” and “computer program product” asused herein generally refers to any media that is involved in providingone or more sequences or one or more instructions to processor 204 forexecution. Such instructions, generally referred to as “computer programcode” (which may be grouped in the form of computer programs or othergroupings), when executed, enable the computing system 200 to performfeatures or functions of embodiments of the present invention. These andother forms of computer-readable media may take many forms, includingbut not limited to, non-volatile media, volatile media, and transmissionmedia. Non-volatile media includes, for example, solid state, optical ormagnetic disks, such as storage device 210. Volatile media includesdynamic memory, such as memory 206. Transmission media includes coaxialcables, copper wire, and fiber optics, including connectivity to bus202.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, a RAM, PROM, and EPROM, aFLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to processor 204 forexecution. For example, the instructions may initially be carried onmagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computing system 200 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detectorcoupled to bus 202 can receive the data carried in the infra-red signaland place the data on bus 202. Bus 202 carries the data to memory 206,from which processor 204 retrieves and executes the instructions. Theinstructions received by memory 206 may optionally be stored on storagedevice 210 either before or after execution by processor 204.

Those skilled in the art will recognize that the operations of thevarious embodiments may be implemented using hardware, software,firmware, or combinations thereof, as appropriate. For example, someprocesses can be carried out using processors or other digital circuitryunder the control of software, firmware, or hard-wired logic. (The term“logic” herein refers to fixed hardware, programmable logic and/or anappropriate combination thereof, as would be recognized by one skilledin the art to carry out the recited functions.) Software and firmwarecan be stored on computer-readable media. Some other processes can beimplemented using analog circuitry, as is well known to one of ordinaryskill in the art. Additionally, memory or other storage, as well ascommunication components, may be employed in embodiments of theinvention.

It will be appreciated that, for clarity purposes, the above descriptionhas described embodiments of the invention with reference to differentfunctional units and processors. However, it will be apparent that anysuitable distribution of functionality between different functionalunits, processors or domains may be used without detracting from theinvention. For example, functionality illustrated to be performed byseparate processors or controllers may be performed by the sameprocessor or controller. Hence, references to specific functional unitsare only to be seen as references to suitable means for providing thedescribed functionality, rather than indicative of a strict logical orphysical structure or organization.

Various embodiments of methods and systems for the analysis ofgenotyping data according to the present teachings may utilize variousembodiments of a cycler instrument as depicted in the block diagramshown in FIG. 3.

FIG. 3 is a block diagram that illustrates a quantitative polymerasechain reaction (qPCR) instrument 300, upon which embodiments of thepresent teachings may be implemented. PCR instrument 300 may include aheated cover 330 that is placed over a plurality of samples 340contained in a sample support device (not shown). In variousembodiments, a sample support device may be a glass, metal or plasticslide with a plurality of sample regions, which sample regions have acover between the sample regions and heated cover 330. Some examples ofa sample support device may include, but are not limited to, amulti-well plate, such as a standard microtiter 96-well, a 384-wellplate, a micro device capable of processing thousands of samples peranalysis, such as various microfluidic devices, microcard devices, andmicro chip devices. The sample regions in various embodiments of asample support device may include depressions, indentations, holes,ridges, and combinations thereof, patterned in regular or irregulararrays formed on the surface of the substrate. Various embodiments ofqPCR instruments include a sample block 350, elements for heating andcooling 360, a heat exchanger 370, control system 380, and userinterface 390. In the present teachings, reference is made to end-pointanalysis. For PCR instrumentation dedicated to performing end-pointanalysis, detection is not done until the thermal cycling is completed.Such PCR instrumentation would generally not include imager 310 andoptics 320.

For qPCR instrumentation depicted by FIG. 3, detection is performedduring the run time of the analysis of biological samples. A detectionsystem may have an illumination source (not shown) that emitselectromagnetic energy, a detector or imager 310, for receivingelectromagnetic energy from samples 340 in a sample support device, andoptics 320 used to guide the electromagnetic energy from each sample toimager 310. For embodiments of PCR instrumentation according to thepresent teachings, control system 380 may be used to control thefunctions of the detection system, heated cover, and thermal blockassembly. Control system 380 may be accessible to an end user throughuser interface 390. Also a computer system 200, as depicted in FIG. 2may serve as to provide the control function to various PCRinstrumentation according to the present teachings, as well as providingthe user interface function. Additionally, computer system 200 of FIG. 2may provide data processing, display and report preparation functions.All such instrument control functions may be dedicated locally to PCRinstrumentation, or computer system 200 of FIG. 2 may provide remotecontrol of part or all of the control, analysis, and reportingfunctions.

As previously mentioned, various embodiments of systems and methodsaccording to the present teachings may have a computer in direct controland communication with a thermal cycling instrument. Various embodimentsof methods and systems for the analysis of genotyping data according tothe present teachings may utilize various embodiments of networking athermal cycling instrument, as shown in FIG. 4.

FIG. 4 is a diagram of a system 400 for networking qPCR instruments,upon which embodiments of the present teachings may be implemented.System 400 includes network 410, two or more qPCR instruments 420, andprocessor 430. The two or more qPCR instruments 420 and processor 430are in communication. This communication can include the exchange ofdata or control information, for example.

Processor 430 can receive a labeling probe intensity from each of thetwo or more qPCR instruments 420. In other words, processor 430 canmonitor two or more experiments from the two or more qPCR instruments420 at substantially the same time. Communication between processor 430and the two or more qPCR instruments 420 is not limited to PCR probeintensities, processor 430 can send and receive PCR experiment filesused to control two or more qPCR instruments 420 and any data producedby the two or more qPCR instruments 420. FIG. 4 shows that system 400provides a one-to-many relationship between processor 430 and the two ormore qPCR instruments 420.

In various embodiments, system 400 provides a one-to-many relationshipbetween one qPCR instrument and two or more processors (not shown). Forexample, system 400 allows one qPCR instrument to send the same measuredprobe intensity from a PCR experiment to two or more processors. Inother words, system 400 allows one qPCR instrument to be monitored bytwo or more processors.

In various embodiments, processor 430 displays a listing of all PCRinstruments connected to network 410. This listing can be used, forexample, to select instruments to actively monitor. This listing can bedisplayed next to a window that shows the instruments currently beingmonitored. In various embodiments, during the creation of a PCRexperiment, this listing may also be used to select the instrument onwhich the experiment will be conducted. Processor 430 creates anexperiment for one or more of the two or more qPCR instruments 420 bycreating a file that includes all of the parameters for the experiment.This file is then sent across network 410 to one of two or more qPCRinstruments 420. The listing of all PCR instruments connected to thenetwork is, for example, a pull down selectable list on a screen used toenter information for an experiment file.

In various embodiments, processor 430 displays a listing of the two ormore PCR instruments it is actively monitoring. This listing may be usedto select an instrument from which additional status information about aPCR experiment can be obtained.

In various embodiments, processor 430 can be used to create two or moreexperiments to be run on the same PCR instrument in a sequence. Such twoor more experiments are batch experiments, for example. Batchexperiments are created for a PCR instrument that includes, for example,a robotic arm that can load two or more sample plates sequentially.Processor 430 displays one or more selectable parameters that can beused to create two or more PCR experiments that are to be run in batchmode, for example. Alternatively, in various embodiments, processor 430provides a command line interface that can be used to create two or morePCR experiments that are to be run in batch mode.

In various embodiments, processor 430 displays a selectable parameterthat enables automatic export of data files from a PCR experiment.Typically a qPCR instrument stores the output data from an experiment onthe PCR instrument. This output is then retrieved by accessing the qPCRinstrument and requesting the data using a processor, such as processor430. In batch processing throughput can be increased by instructing theqPCR instrument to automatically send output data at the end of anexperiment to processor 430. The qPCR instrument can be instructed toexport a file by placing a parameter in the experiment file created byprocessor 430, for example. In addition to requesting export of outputdata files, processor 430 can specify other parameters that include, butare not limited to, naming conventions for export files and locations ona storage medium where the output data should be sent. A namingconvention can include adding a barcoded plate number to the output datafile name, for example.

In various embodiments, processor 430 can display a window in whichpreferences for date, time, and numeric separation formats of data canbe specified.

In various embodiments, processor 430 can display a window that allows afile that includes calibration information to be selected for aparticular PCR experiment that is created.

In various embodiments, processor 430 can display for a gene expressionexperiment a selectable list of sample types, can display a selectablelist of gene targets, and can plot threshold cycles of each selectedgene target as function of sample type as a quality control.

While the above embodiments have been recited with respect to variousembodiments of networked systems, one of ordinary skill in the art willrecognize the applicability of the teachings to a single qPCR instrumentin communication with a processor, memory, and display.

Visualization Tool: Dynamic Display of qPCR Data

As described above, although PCR systems receive and analyze data, theygenerally only display useful or discriminatory information at theend-point or after completion of the PCR experiment. For example, inquantitative PCR-based genotyping, intensity data can be obtaineddynamically during runtime for two allele-specific labeling probes ateach cycle. Traditionally, this qPCR data is viewed as an amplificationplot. An amplification plot shows the intensities of one allele-specificlabeling probe for a plurality of samples plotted against time, withintervals of cycle number, for the convenience to an end user. As one ofordinary skill in the art of cycle instrumentation is apprised, data maybe collected from a detector using a variety of protocols, and is takenover time. An amplification plot, however, does not generally show theextent of allelic discrimination.

Traditionally, the extent of allelic discrimination has been shown inallelic discrimination plots that use only end-point data. An allelicdiscrimination plot shows the intensities of a first allele-specificlabeling probe for a plurality of samples plotted against theintensities of a second allele-specific labeling probe for the sameplurality of samples, for example. The extent of allelic discriminationin allelic discrimination plots is shown as distances between clustersof data. Further, allelic discrimination plots can also show the resultsof clustering algorithms that call genotypes by labeling clusters withcalled allele values.

FIG. 5 is an exemplary amplification plot 500, upon which embodiments ofthe present teachings may be implemented. The intensities shown in FIG.5 are for the allele-specific labeling probe to provide for fluorescentdetection, for example. Amplification of the allele-specific labelingprobe for a large number of samples is apparent in plot 500. However,the extent of allelic discrimination is not apparent.

FIG. 6 is an exemplary allelic discrimination plot 600 of end-point datafrom the same experiment that produced the data of FIG. 5 where allelicdiscrimination is apparent, upon which embodiments of the presentteachings may be implemented. The extent of allelic discrimination isapparent in plot 600 from the distances between clusters of data 610,620, and 630. Further, the data shown in plot 600 was additionallyprocessed so that the samples were specifically labeled as undetermined640, first allele 650, second allele 660, both alleles 670, or notemplate control (NTC) 680 using a genotyping clustering algorithm, forexample. Although the end-point data of plot 600 shows the extent ofallelic discrimination, not all end-point data is guaranteed to showsimilar results.

FIG. 7 is an exemplary allelic discrimination plot 700 of end-point datafrom a different PCR experiment that used the same samples as were usedto produce the data of FIG. 5 where allelic discrimination is notapparent, upon which embodiments of the present teachings may beimplemented. The data of plot 700 of FIG. 7 and the data for plot 600 ofFIG. 6 may be generated using different assay designs or differentsample preparations, for example. Plot 700 of FIG. 7 shows littleallelic discrimination and almost all sample values are located in onelarge area 710. In addition, a clustering algorithm labeled all datapoints of plot 700 as undetermined.

According to various embodiments of the present teachings, the dimensionof time shown in amplification plots, which is frequently displayed ascycle number, may be added to the allelic discrimination capability ofallelic discrimination plots to increase the confidence of welldiscriminated results such as those shown in FIG. 6 or to troubleshootundetermined results such as those shown in FIG. 7. Two or more allelicdiscrimination plots at corresponding two or more times may be displayedin succession to show the progression of allelic discrimination as afunction of cycle number. These two or more allelic discrimination plotscan be displayed under the control of a user in an interactive processprovided by a user interface, or as a user selection to display thetime-based data as a video, for example. These two or more allelicdiscrimination plots can be displayed dynamically during run time as thedata is collected or after data collection from stored data. FIGS. 8-10are three allelic discrimination plots corresponding to three differenttimes during thermal cycling that show the progression of allelicdiscrimination as a function of cycle number. According to variousembodiments of the present teachings, once the data has been stored, itmay be reviewed at any time selected by a user. In various embodiments,time may be displayed as cycle number.

FIG. 8 is an exemplary allelic discrimination plot 800, according tovarious embodiments of the present teachings, which was produced usingthe data of FIG. 6. In FIG. 8, a graphical user interface according tothe present teachings is shown presenting a user with a selection oftime by selecting a cycle number; shown as cycle number 30. Plot 800shows that at cycle number 30 the clusters of data are well-defined eventhough the distances between them are small.

Confidence or quality values for this data are also shown in plot 800.Sample table 820 provides a cell for each sample where a variety ofinformation about the sample may be displayed, as will be discussed inmore detail subsequently. In cell 830, for example, a confidence valueof 90 percent is shown for a particular sample. Sample table 820 caninclude confidence values for all of the samples (not shown), forexample. Plot 800 also includes a value for a blank or no templatecontrol (NTC) 840. According to various embodiments, an origin may bedefined by a negative control. A negative control may be referred to asa non-template control (NTC), which is a sample not containing thetarget genomic locus of interest. For various embodiments of agenotyping assay, the negative control or NTC may contain nooligonucleotide material, and may contain, for example, but not limitedby, all the reagents brought to a volume equal to biological samplesbeing assayed. According to other embodiments of a genotyping assay, theNTC may contain, for example, but not limited by, an oligonucleotidesample validated not to contain the sequences of a target genomic locusbeing assayed. As one of ordinary skill in the art is apprised, such NTCsamples may still produce a background signal that may be detected. Inthat regard, one or more NTC samples may be used to define an origin aswell as a baseline from which the angles of the samples emitting adiscrete set of signals for each of three possible allelic can bedetermined. In various embodiments, a plurality of NTC samples may beused to determine an origin and a baseline thereby. As one of ordinaryskill in the art is apprised, there may be a variety of ways to processthe data from a plurality of NTC samples to determine a value for theorigin, including, but not limited by, the determination of the mean,the median, and the centroid of a plurality of NTC samples. NTC 840 isused to confirm that no intensity is found if no sample is present.

Confidence or quality values can also be displayed a part of a listingof information. Line 850 shows a confidence or quality value of 90percent listing along with the well label, intensity values for eachallele, and the genotype call. Lines can be displayed for all of thesamples (not shown), for example.

FIG. 9 is an exemplary allelic discrimination plot 900 at cycle number35 from the same experiment that produced the data of FIG. 6, upon whichembodiments of the present teachings may be implemented. Plot 900 showsthat at cycle number 35 the clusters of data are still well-defined andthe distances between clusters are growing. The confidence value for thesample shown in cell 830 is even higher.

FIG. 10 is an exemplary allelic discrimination plot 1000 at cycle number40 from the same experiment that produced the data of FIG. 6, upon whichembodiments of the present teachings may be implemented. Plot 1000 showsthat at cycle number 40 the clusters of data are again stillwell-defined and the distances between clusters have grown even more.The confidence value for the sample shown in cell 830 is again evenhigher.

FIGS. 8-10 show that displaying two or more allelic discrimination plotsin response to input from an end user can provide additional informationand enable a number of additional or alternative processing steps. Forexample, FIGS. 8-10 provide confidence to a user that the end-point datashown in FIG. 6 is correct.

Also, the well-defined clusters and high confidence values shownthroughout the progress of reaction may suggest that the PCR experimentof FIGS. 6 and 8-10 may yield reliable results with less than thetypically run 40 cycles; for example as few as 30 or 35 cycles.Therefore, certain assays for genotype discrimination can be modifiedand improved by viewing two or more allelic discrimination plots as afunction of time. In various embodiments, if good results with highconfidence are recognized by the PCR system as it is thermal cycling, itcan stop the run and call the genotypes. This automatic step canincrease experiment throughput by decreasing the thermal cycling timebased on the qPCR data.

Additionally, the examples shown in FIGS. 8-10 include allelic labelsgenerated by a genotyping algorithm. In various embodiments, two or moreallelic discrimination plots can be displayed as a function of time, anddo not include information from a genotyping algorithm. As a result,analyzing the data using various embodiments of the present teachingsmay provide an end user an ability to call the genotype manually. Forexample, by using displayed information, such as the well-definedclusters, the distances between clusters, and the confidence values, mayprovide an end user with sufficient information to make genotype calls.

Finally, in one traditional genotyping workflow the intensities ofallele-specific labeling probes for each sample are read using a qPCRinstrument before any thermal cycling is performed, which is oftenreferred to as pre-read data acquisition. The samples are then cycledfor a fixed number of cycles on a PCR instrument that does not includethe capability of reading the intensities of allele-specific labelingprobes of the samples. After the fixed number of cycles, the samples arereturned to the qPCR system and a post-read or end-point dataacquisition is performed. The genotype can be called in such a workflowby comparing allelic discrimination plots of the post-read and pre-readacquisitions. In various embodiments, displaying two or more allelicdiscrimination plots from a qPCR system can eliminate the need forpost-read and pre-read acquisitions in a genotyping workflow. The two ormore allelic discrimination plots can provide enough information to callthe genotype.

FIGS. 8-10 show the progression of allelic discrimination as a functionof cycle number over three separate plots. In various embodiments,information provided in these three separate plots can be assembled anddisplayed in one plot. For example, the data of FIGS. 8-10 can beplotted on one allelic discrimination plot and lines can be drawnbetween the different cycle number values. These lines may be referredto as trajectory lines.

FIG. 11 is an exemplary allelic discrimination plot 1100 at cycle number40 from the same experiment that produced the data of FIG. 5 showingtrajectory lines 1110 representing data from previous cycle numbers,upon which embodiments of the present teachings may be implemented.Trajectory lines 1110 show the progression of allelic discriminationover cycle number. In various embodiments, trajectory lines 1110 can beadded to or removed from plot 1000 under user control, for example.

The data in plot 1100 is shown in Cartesian coordinates. In variousembodiments, this data can be plotted in polar coordinates. Polarcoordinates are more relevant to some clustering algorithms, forexample.

Trajectory lines 1110 converge to the data of the first cycle number atorigin 1120. This convergence suggests that little or no intensities areobserved at the beginning of thermal cycling. Traditionally, similarinformation has been provided by adding a blanks or NTCs to a platewell. Therefore, in various embodiments, providing trajectory lines forearly cycle numbers can eliminate the need for blanks or NTCs.Eliminating blanks or NTCs increases the space on a plate for samplesand can, therefore, increase experiment throughput.

Plot 1100 shows results from looking at one particular site on a genome.In various embodiments, plot 1100 can display data resulting from theanalysis of two or more sites on the genome. This data is calledmultiplexed data, for example. The trajectories of the data from eachsite on the genome can have a different origin, for example. Thetrajectories of the data from each site on the genome can also have adifferent scale of intensity.

A clustering algorithm was run on the data shown in FIGS. 8-10 beforethe data was displayed. As described above, depending on the confidenceat each cycle number for which data is displayed, the genotypes can becalled. Similarly, in various embodiments, the trajectories of anallelic discrimination plot can be clustered into groups and thesegroups can be used to call genotypes. A hybrid recursive matching (HRM)algorithm can be used for example.

Generally, a clustering algorithm requires a certain number of samplesclustered together to be able to call genotypes. For example, insingle-nucleotide polymorphism (SNP) genotyping, 24 samples can berequired for a given assay to cluster the samples into two homozygousclusters and one heterozygous cluster. In various embodiments, genotypescan be called from a single trajectory. For example, a trajectory thatmoves straight and then bends to the right can be called homozygous to afirst allele, a trajectory that moves straight and then bends to theleft can be called homozygous to a second allele, and a trajectory thatdoes not curve can be called heterozygous. This type of trajectorypattern recognition can eliminate the need for data clusteringalgorithms.

In various embodiments, analyzing the trajectories of allelicdiscrimination plots can be used to rescue rare SNPs. With a rare SNP,almost all trajectories are homozygous. As a result, sample values areclose together and a clustering algorithm has no basis to call thecluster. Trajectory lines, however, show that all data points started atthe same point and migrated in one direction. As a result, the genotypecan be called from the trajectory lines.

In various embodiments, plots showing the progression of allelicdiscrimination can be used to troubleshoot undetermined results orrescue precious samples. FIG. 7 is an allelic discrimination plot ofend-point data that provides undetermined results. However, because FIG.6 is plotted from same samples used in FIG. 5, it is known thatgenotyping these samples is possible.

FIG. 12 is an exemplary allelic discrimination plot 1200 at cycle number40 from the same experiment that produced the data of FIG. 6 showingtrajectory lines 1210 representing data from previous cycle numbers,upon which embodiments of the present teachings may be implemented.Trajectory lines 1210 show that three clusters did separate at earliercycle numbers. Trajectory lines 1210 can, therefore, be used totroubleshoot the data of FIG. 7. If the data of FIG. 7 is created fromprecious samples, trajectory lines 1210 of FIG. 12 can be used to rescuethe data. Precious samples are samples that provide very small sampleinput. Precious samples include sample input from forensics, a fewcells, or laser capture micro-dissection, for example. In variousembodiments, trajectory lines 1210 are used to rescue the data from aprecious sample by allowing the selection of a cycle number were thedata was of sufficient quality to allow genotypes to be called. The dataat this cycle number is then used to call the genotypes.

FIG. 12 shows that clusters of data separate during thermal cycling andthen converge again near the end. Without showing the progression ofallelic discrimination over cycle number, the separation and convergenceare not apparent. In various embodiments, a PCR system can automaticallymonitor separation and convergence during a PCR run. If, for example, atransition from separation to convergence is found, the PCR system canadjust the thermal cycling to increase separation. For example, a PCRsystem can increase the annealing temperature.

In various embodiments of systems and methods according to the presentteachings, a user interface providing dynamic display of genotyping dataincludes features providing the interaction between a sample table and aplot of genotyping data. In various embodiments, such a sample table mayhave a wide range of information associated with each sample representedin the table. According to various embodiments, the information mayrelate to a wide range of attributes associated with each sample. Invarious embodiment, such sample attributes can include, but are notlimited to, sample, biological group, target, task, input quantity,time, time unit, sample source, treatment, and comments by an end user.For various embodiments, the information associated with each sample ina sample table may be related to a protocol under which the sample wasrun on a qPCR instrument. In various embodiments of systems and methodsof the present teachings, an end user may select a sample or samplesrepresented on the sample table in any fashion desired; each samplehaving a wealth of sample information associated with it, and theselected sample or samples may be displayed on the plot of genotypingdata. According to various embodiments, an end user may scroll oversamples entered in the sample table, and view the information related toeach sample. In that regard, through such a visualization tool, an enduser may dynamically understand the impact of a variety of experimentalconditions on the outcome of a genotyping experiment.

For example, in FIGS. 13A-13C, the data for a plurality of samplesanalyzed in a genotyping experiment are displayed. In FIG. 13A, threepopulations of samples 1310, 1320, and 1330, representing clusters ofsamples for homozygous (2/2), homozygous (1/1), and heterozygous (½),respectively. One of ordinary skill in the art will recognize a sampletable representing an assay format for analyzing as many as 384 samples,where each sample is represented in a cell in an alpha-numericdesignation for rows and columns. As can be seen from the sample table,there are 91 samples analyzed (U=unknown), 2 non-template controls (N),and three positive controls for the target gene being analyzed (1/1,2/2, and ½). In FIG. 13B, an end user may select a particular group ofsamples, and have them highlighted for view and analysis on the plot. InFIG. 13C, an end user may select a region of particular interest on thesample table, and the associated samples are displayed on the plot. Invarious embodiments, an end user can scroll over the sample table andview the information associated with each sample (not shown) In thisfashion, a ready analysis of conditions impacting genotyping results maybe imparted to an end user.

According to various embodiments of systems and methods of the presentteachings, the qPCR data sets taken over the course of the entire runtime of a genotyping assay provide snap-shots over time of the progressof each sample in a plurality of samples. In various embodiments ofsystems and methods of the present teachings, a visualization toolpresented in response to input from an end user may provide for astep-wise display of genotyping data, or it may provide for the displayrun as a video. In various embodiments according to the presentteachings, such a visualization tool may provide an end user a dynamicreview of all data as a function of time as an aid to analysis ofgenotyping data.

For example, FIG. 14A represents an analysis done in which three clusterof data 1610, 1620, and 1630, have been clearly called. However,clusters 1615 and 1625 have not been called by an algorithm used toanalyze genotyping data, representing 6 uncalled samples in the set atend point. As displayed in FIG. 14B, an end user may select a time,represented for convenience to an end user as cycle number, to look atthe status of the samples at a time earlier than the end-point time. Ascan be seen by inspection of FIG. 14B, at cycle 34, the number ofsamples for which no call can be made with confidence by a genotypingalgorithm has dropped to three uncalled samples in the set of samples.Finally, in FIG. 14C, at 30 cycles, the number of uncalled samples hasdropped to two samples. While for the purpose of explanation, FIGS.14A-14C were presented in a reverse time sequence, according to variousembodiments of systems and methods according to the present teachings,an end user may select any time slice of the progress of a genotypingassay for a plurality of samples in any order. Additionally, one ofordinary skill in the art will appreciate that while these data areexemplary of improving outcome by selecting a time represented by lessthan 40 cycles, analysis outcome may be also be enhanced in certaininstances for a plurality of samples run for a greater number of cycles.For example for a gene target in a rare or damaged sample, an end usermay select a protocol for greater than 40 cycles. In such a case, allthe data is available to analyze using various embodiments of avisualization tool according to the present teachings.

The various implementations of the present teachings have been presentedfor purposes of illustration and description. They are not exhaustiveand do not limit the present teachings to the precise form disclosed. Onthe contrary, the present teachings encompass various alternatives,modifications, and equivalents, as will be appreciated by those skilledin the art. Modifications and variations are possible in light of theabove teachings or may be acquired from practicing of the presentteachings. Additionally, the described implementation includes softwarebut the present teachings may be implemented as a combination ofhardware and software or in hardware alone. The present teachings may beimplemented with both object-oriented and non-object-orientedprogramming systems.

Further, in describing various embodiments, the specification may havepresented a method and/or process as a particular sequence of steps.However, to the extent that the method or process does not rely on theparticular order of steps set forth herein, the method or process shouldnot be limited to the particular sequence of steps described. As one ofordinary skill in the art would appreciate, other sequences of steps maybe possible. Therefore, the particular order of the steps set forth inthe specification should not be construed as limitations on the claims.In addition, the claims directed to the method and/or process should notbe limited to the performance of their steps in the order written, andone skilled in the art can readily appreciate that the sequences may bevaried and still remain within the spirit and scope of the variousembodiments.

What is claimed:
 1. A computer-readable storage medium encoded withinstructions, executable by a processor, for a visualization tool forgenotyping data, the instructions comprising: receiving by the processora first data set at a first time, wherein the first data set comprises afirst intensity signal for a first probe and a first intensity signalfor a second probe; receiving by the processor a second data set at asecond time, wherein the second data set comprises a second intensitysignal for the first probe and a second intensity signal for the secondprobe; presenting an end user with a visualization tool, wherein thevisualization tool provides a dynamic display of the data selected from:generating by the processor a first plot from the first data set anddisplaying the first plot; and; generating by the processor a secondplot from the second data set and displaying the second plot; whereinthe dynamic display of the data provides for analysis of genotypingdata.
 2. The computer-readable storage medium of claim 1, wherein theselection of the dynamic display of the data is in response to inputfrom the end user.
 3. The computer-readable storage medium of claim 2,wherein the input from the end user is a step-wise selection of thedisplay of the data.
 4. The computer-readable storage medium of claim 2,wherein the input from the end user is a selection of a video display ofthe data.
 5. The computer-readable storage medium of claim 1, whereinthe processor is in communication with a thermal cycling instrument. 6.The computer-readable storage medium of claim 5, wherein the processorreceives the first data set and the second data set after thermalcycling has been completed.
 7. The computer-readable storage medium ofclaim 5, wherein the processor receives the first data set and thesecond data set during thermal cycling.
 8. The computer-readable storagemedium claim 1, wherein the display comprises trajectory lines betweenthe first data set and the second data set.
 9. A computer implementedmethod for a visualization tool for genotyping data, the methodcomprising: receiving by a processor a first data set at a first time,wherein the first data set comprises a first intensity signal for afirst probe and a first intensity signal for a second probe; receivingby a processor a second data set at a second time, wherein the seconddata set comprises a second intensity signal for the first probe and asecond intensity signal for the second probe; processing the data set ona computer to analyze genotyping data, the processing comprising:presenting an end user with a visualization tool, wherein thevisualization tool provides a dynamic display of the data selected from:generating by the processor a first plot from the first data set anddisplaying the first plot; and; generating by the processor a secondplot from the second data set and displaying the second plot; whereinthe dynamic display of the data provides for analysis of genotypingdata.
 10. The computer implemented method of claim 9, wherein theselection of the dynamic display of the data is in response to inputfrom the end user.
 11. The computer implemented method of claim 10,wherein the input from the end user is a step-wise selection of thedisplay of the data.
 12. The computer implemented method of claim 10,wherein the input from the end user is a selection of a video display ofthe data.
 13. The computer implemented method of claim 9, wherein theprocessor is in communication with a thermal cycling instrument.
 14. Thecomputer implemented method of claim 13, wherein the processor receivesthe first data set and the second data set after thermal cycling hasbeen completed.
 15. The computer implemented method of claim 13, whereinthe processor receives the first data set and the second data set duringthermal cycling.
 16. The computer implemented method of claim 9, whereinthe display comprises trajectory lines between the first data set andthe second data set.
 17. A system comprising: a processor; and a memoryin communication with the processor; the memory storing instructionsfor: receiving by the processor a first data set at a first time,wherein the first data set comprises a first intensity signal for afirst probe and a first intensity signal for a second probe; receivingby the processor a second data set at a second time, wherein the seconddata set comprises a second intensity signal for the first probe and asecond intensity signal for the second probe; presenting an end userwith a visualization tool, wherein the visualization tool provides adynamic display of the data selected from: generating by the processor afirst plot from the first data set and displaying the first plot; and;generating by the processor a second plot from the second data set anddisplaying the second plot; wherein the dynamic display of the dataprovides for analysis of genotyping data.
 18. The system of claim 17,wherein the selection of the dynamic display of the data is in responseto input from the end user.
 19. The system of claim 18, wherein theinput from the end user is a step-wise selection of the display of thedata.
 20. The system of claim 18, wherein the input from the end user isa selection of a video display of the data.
 21. The system of claim 17,wherein the processor is in communication with a thermal cyclinginstrument.
 22. The system of claim 21, wherein the processor receivesthe first data set and the second data set after thermal cycling hasbeen completed.
 23. The system of claim 21, wherein the processorreceives the first data set and the second data set during thermalcycling.
 24. The system of claim 21, wherein the display comprisestrajectory lines between the first data set and the second data set.